Every day different sources (including machines, and sensors, and humans), constantly generate data. As data generation increases, so does the need to store, manage, organize, protect, and analyze it.
Data management is one of the most time-consuming aspects of running software tests. It’s crucial to be efficient and ensure you don’t waste time doing things that don’t directly improve data management capabilities.
Managing test data can be a learning curve for any enterprise to handle independently. Luckily, there are reliable ways to optimize your approach to test data management and help speed up the process.
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How Do You Manage Test Data?
Building and running software tests can be complex, and especially important when creating test data that is realistic and relevant to the customer’s needs. The purpose of test data management is to provide efficient solutions for managing this essential aspect of any software testing project.
When starting with automation, it’s tempting to automate everything at once. However, this can quickly lead to unmanageable code and many headaches down the road.
A better approach is to start small by ring-fencing data for automation. Ring-fencing means creating a separate data set that you can use specifically for testing purposes.
This data should be static and unchanging, so your test cases are always reliable. You can then gradually expand the scope of your automated test suite as time goes on.
You should also consider validation rules to help ensure that the data entered in a field is accurate and consistent. By creating validation rules, you can specify the type of data entered in an area, the format of the data, and whether the field is required.
What Are the Challenges of Test Data Management?
Poor data management can lead to wasted time and money, missed deadlines, and significant bugs that slip through the cracks. As a result, it’s essential to understand the biggest challenges of test data management and how you can overcome them.
Data Validity and Consistency
One challenge that can be overcome when managing test data is maintaining data validity and consistency. It means ensuring that the data entered is accurate and up-to-date, and that it conforms to the rules set by the system.
This action can be challenging to do manually, especially as the data set grows in size. You need to know what fields are essentia, which are not, and how they interact.
You also need to know how changes in one field will affect other areas on the table. There are many validation checks you can put in place to help with this problem. For instance, you can set a hard limit on input values or use a preview option before submitting new information.
Data Privacy
When organizations are dealing with large amounts of customer data, they need to be sure that this data is kept secure and confidential.
Achieving this can be challenging when managing test data, as organizations often store data in various locations and formats. There are many different pieces of information to keep track of, making it difficult for companies to maintain an accurate data record.
One way around this issue is using a third-party provider who can take care of all aspects of test data management, including inventory tracking and disposal.
Data Selection and Sub-setting
Selecting and subsetting the correct data to use for testing can be challenging, because you need to ensure you have enough data to cover all test cases.
However, you also don’t want so much data that it bogs your system. You can take some time to look at and analyze your actual data before you decide which subsets will work best for your needs.
Test Data Management Best Practices
Test data management (TDM) best practices are based on managing test data in a cost-effective manner and depend on your current infrastructure and technology stack. Let’s take an in-depth look at some of these practices that can help accelerate your test data management.
Begin With the Test Data Strategy
Creating a data strategy is one of the most effective practices for effective test data management. This practice will help ensure that you have the correct data for your testing needs and that you organize it in a way that makes sense for your team.
It’s also helpful to prioritize what types of data are necessary for different phases of the software development lifecycle. Then you can plan where this data will be stored and how you can share it with other team members and stakeholders outside of IT.
You should review test data strategies annually (or as needed) to keep up with changes in technology and project scope.
Discover Test Data From Multiple Data Sources
To have an effective test data management strategy, you need to be able to discover test data from multiple data sources. These sources can include databases, files, and even applications.
Doing this can give you a complete picture of your test data and ensure that you use the most up-to-date and accurate information. It also ensures no hidden dependencies between data sets, which could lead to erroneous or inaccurate results.
For example, a company may have an application with many users that features attributes such as first name, last name, email address, and password.
Suppose you included one user in a validation suite with test data that had the username and password attributes. In that case, these two fields would likely not be matched correctly due to the lack of unique identifiers such as email address or phone number.
Identify Sensitive Test Data and Mask It
When creating or updating test data, it’s essential to identify any sensitive information and mask it accordingly. Data masking will protect individuals’ privacy and help ensure compliance with regulations.
There are a few different ways to mask data, such as using fictitious names or randomizing values. Some customers may also need to encrypt their sensitive data before storing it in your system. The encryption key can be stored in an additional file that only people with proper authorization can access.
Utilize Test Data Generation Tools
There are many test data generation tools available that can help create realistic test testers. Utilizing these tools can help ensure that your test data is of high quality and accurately represents the live data.
Additionally, using test data generation tools can help save time and money by reducing the need for manual test data creation. Test data management tools make it easy to create a large set of test data quickly.
These tools often include templates you can apply to produce realistic sets of different types of data. For example, you can have templates for creating individual and company profiles with values for address fields, phone numbers, and credit card information.
Refresh Data Sources Regularly
It’s essential to keep your test data up-to-date, especially if you’re testing web applications that rely on live data. One way to do this is to refresh your data sources regularly.
You can refresh your data sources manually or in an automated fashion, but ensuring your test data is as fresh as possible is essential. Test data needs to reflect the latest changes in a product and its features.
If this is not the case, your tests will only give you a glimpse of what was happening during their creation rather than the current state. You’ll also find yourself retesting the same bugs over and over again because those bugs still exist in an older version of the software.
Automated refreshing ensures that you can fix any issues promptly and stay focused on creating new tests for different bugs.
Virtualize Whenever Possible
Virtualization refers to the process of replicating a physical server in software. You can virtualize applications and data to execute the workloads in an isolated, hardware-independent environment on the same or different hardware from the original server.
For instance, you can dynamically allocate hardware resources such as CPUs, memory, and I/O devices to satisfy the needs of each virtual machine.
In test data management, you can use virtualization for database mirroring, database replication, and other similar services that enable rapid provisioning of parallel environments.
The advantages of virtualizing test environments include flexibility in application sizing, quicker provisioning times, and better resource utilization. You can quickly turn off underutilized servers without affecting performance on other servers.
Leverage a Platform-based Approach
A platform-based approach to test data management can help you save time and money while ensuring the quality of your data. As your business grows, you can quickly scale data management operations.
A platform-based approach allows you to tailor your data management processes to meet your organization’s unique needs. For example, you may choose to dedicate one person or team to be responsible for managing all test data.
They would be in charge of determining which type of data you should create and when to add it. With this approach, the individual will always have the most up-to-date version of the most relevant information.
Final Thoughts
Managing test data is a crucial part of any software development process. Without proper management, test data can quickly become overwhelming and lead to errors in your code.
The best way to manage test data is to keep it organized and separate from your production data. But this is just one secret to success. Following these best practices will help you manage your test data effectively and efficiently.